Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	118,159
2	South Dakota	107,077
3	Iowa	84,729
4	Wisconsin	84,625
5	Nebraska	80,910
6	Utah	78,280
7	Tennessee	74,076
8	Rhode Island	73,451
9	Idaho	72,766
10	Montana	72,203
11	Wyoming	71,985
12	Illinois	71,175
13	Minnesota	70,461
14	Kansas	69,911
15	Indiana	69,406
16	Arkansas	66,820
17	Nevada	66,304
18	Oklahoma	65,918
19	Alabama	65,763
20	Mississippi	65,296
21	Missouri	62,934
22	Arizona	62,318
23	New Mexico	61,995
24	Louisiana	61,552
25	Alaska	59,666
26	Florida	55,944
27	Texas	54,945
28	Kentucky	54,790
29	Colorado	53,921
30	Georgia	53,464
31	Ohio	53,280
32	South Carolina	53,101
33	Delaware	51,979
34	Michigan	49,611
35	New Jersey	48,918
36	California	47,825
37	Massachusetts	46,939
38	North Carolina	45,754
39	Connecticut	45,657
40	New York	43,765
41	Pennsylvania	43,743
42	Maryland	41,528
43	West Virginia	40,366
44	District of Columbia	37,691
45	Virginia	35,949
46	Puerto Rico	31,952
47	Washington	29,816
48	New Hampshire	26,874
49	Oregon	24,404
50	Hawaii	14,331
51	Maine	14,094
52	Vermont	10,325

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Tennessee	1,252
2	California	1,084
3	Oklahoma	1,064
4	Indiana	850
5	Alabama	823
6	Arkansas	785
7	Nevada	775
8	Ohio	759
9	Utah	732
10	Mississippi	720
11	West Virginia	717
12	Arizona	690
13	Massachusetts	690
14	South Carolina	688
15	North Carolina	671
16	Pennsylvania	644
17	Delaware	632
18	Kansas	627
19	New Mexico	627
20	Wyoming	615
21	Georgia	596
22	Wisconsin	588
23	South Dakota	576
24	Kentucky	568
25	New Hampshire	558
26	New York	547
27	Illinois	544
28	Iowa	540
29	Idaho	525
30	Florida	513
31	Montana	506
32	New Jersey	497
33	Louisiana	489
34	Colorado	488
35	Minnesota	481
36	Nebraska	474
37	Texas	467
38	Alaska	460
39	Missouri	447
40	North Dakota	426
41	Virginia	420
42	Puerto Rico	384
43	Maryland	376
44	District of Columbia	364
45	Oregon	315
46	Michigan	287
47	Washington	281
48	Maine	259
49	Connecticut	250
50	Rhode Island	164
51	Vermont	157
52	Hawaii	120

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,048
2	New York	1,851
3	Massachusetts	1,699
4	North Dakota	1,623
5	Connecticut	1,565
6	South Dakota	1,538
7	Rhode Island	1,533
8	Louisiana	1,514
9	Mississippi	1,481
10	Illinois	1,294
11	Michigan	1,208
12	Iowa	1,137
13	Indiana	1,099
14	Arizona	1,095
15	Pennsylvania	1,090
16	Arkansas	1,072
17	District of Columbia	1,044
18	New Mexico	1,035
19	South Carolina	958
20	Florida	957
21	Georgia	947
22	Nevada	904
23	Maryland	901
24	Alabama	895
25	Texas	894
26	Delaware	885
27	Tennessee	879
28	Minnesota	870
29	Missouri	857
30	Wisconsin	814
31	Montana	809
32	Kansas	803
33	Nebraska	778
34	Colorado	767
35	Idaho	718
36	Ohio	688
37	West Virginia	629
38	Wyoming	606
39	Kentucky	596
40	North Carolina	596
41	California	575
42	Oklahoma	559
43	Virginia	544
44	New Hampshire	482
45	Puerto Rico	428
46	Washington	416
47	Utah	360
48	Oregon	317
49	Alaska	240
50	Maine	217
51	Hawaii	197
52	Vermont	177

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	22
2	Iowa	14
3	Arkansas	13
4	Pennsylvania	13
5	Arizona	11
6	Illinois	11
7	Indiana	11
8	Minnesota	11
9	Nevada	11
10	North Dakota	11
11	Kansas	10
12	New Mexico	10
13	Tennessee	10
14	West Virginia	10
15	Wisconsin	10
16	Alabama	9
17	Michigan	9
18	Mississippi	9
19	Colorado	8
20	Missouri	8
21	Maryland	7
22	Massachusetts	7
23	Rhode Island	7
24	Texas	7
25	Kentucky	6
26	New Hampshire	6
27	New York	6
28	Delaware	5
29	District of Columbia	5
30	Louisiana	5
31	Nebraska	5
32	Oklahoma	5
33	South Carolina	5
34	California	4
35	Florida	4
36	Montana	4
37	New Jersey	4
38	North Carolina	4
39	Ohio	4
40	Oregon	4
41	Puerto Rico	4
42	Idaho	3
43	Maine	3
44	Utah	3
45	Vermont	3
46	Virginia	3
47	Connecticut	2
48	Georgia	2
49	Alaska	0
50	Hawaii	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	260,353	1	99
Norton	Kansas	214,699	2	99
Lincoln	Arkansas	209,843	3	99
Bon Homme	South Dakota	209,390	4	99
Dewey	South Dakota	207,739	5	99
Davidson	Tennessee	88,679	387	87
Richland	South Carolina	58,317	1516	51
York	South Carolina	47,313	2093	33
Orange	California	39,921	2411	23
Pierce	Washington	27,544	2802	10

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	5,974	4	99
Foster	North Dakota	5,919	5	99
Richland	South Carolina	767	1713	45
Davidson	Tennessee	725	1795	42
York	South Carolina	587	2063	34
Orange	California	559	2116	32
Pierce	Washington	337	2564	18

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.